Simplify Big Data Analytics with Amazon EMR
By :
Simplify Big Data Analytics with Amazon EMR
By:
Overview of this book
Amazon EMR, formerly Amazon Elastic MapReduce, provides a managed Hadoop cluster in Amazon Web Services (AWS) that you can use to implement batch or streaming data pipelines. By gaining expertise in Amazon EMR, you can design and implement data analytics pipelines with persistent or transient EMR clusters in AWS.
This book is a practical guide to Amazon EMR for building data pipelines. You'll start by understanding the Amazon EMR architecture, cluster nodes, features, and deployment options, along with their pricing. Next, the book covers the various big data applications that EMR supports. You'll then focus on the advanced configuration of EMR applications, hardware, networking, security, troubleshooting, logging, and the different SDKs and APIs it provides. Later chapters will show you how to implement common Amazon EMR use cases, including batch ETL with Spark, real-time streaming with Spark Streaming, and handling UPSERT in S3 Data Lake with Apache Hudi. Finally, you'll orchestrate your EMR jobs and strategize on-premises Hadoop cluster migration to EMR. In addition to this, you'll explore best practices and cost optimization techniques while implementing your data analytics pipeline in EMR.
By the end of this book, you'll be able to build and deploy Hadoop- or Spark-based apps on Amazon EMR and also migrate your existing on-premises Hadoop workloads to AWS.
Table of Contents (19 chapters)
Preface
Section 1: Overview, Architecture, Big Data Applications, and Common Use Cases of Amazon EMR
Free Chapter
Chapter 1: An Overview of Amazon EMR
Chapter 2: Exploring the Architecture and Deployment Options
Chapter 3: Common Use Cases and Architecture Patterns
Chapter 4: Big Data Applications and Notebooks Available in Amazon EMR
Section 2: Configuration, Scaling, Data Security, and Governance
Chapter 5: Setting Up and Configuring EMR Clusters
Chapter 6: Monitoring, Scaling, and High Availability
Chapter 7: Understanding Security in Amazon EMR
Chapter 8: Understanding Data Governance in Amazon EMR
Section 3: Implementing Common Use Cases and Best Practices
Chapter 9: Implementing Batch ETL Pipeline with Amazon EMR and Apache Spark
Chapter 10: Implementing Real-Time Streaming with Amazon EMR and Spark Streaming
Chapter 11: Implementing UPSERT on S3 Data Lake with Apache Spark and Apache Hudi
Chapter 12: Orchestrating Amazon EMR Jobs with AWS Step Functions and Apache Airflow/MWAA
Chapter 13: Migrating On-Premises Hadoop Workloads to Amazon EMR
Chapter 14: Best Practices and Cost-Optimization Techniques
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